1. Field of Invention
The present invention relates generally to object recognition, and more particularly to a method to deform a template of an object into an observed deformation of the object.
2. Summary of the Invention
Automated image registration is required whenever information obtained from different views of an object needs to be combined or compared. Given two images, one is looking for a transformation, such that one transformed image becomes similar to the second image. While an extensive amount of work has been done on this problem the fundamental question of how to reliably and efficiently estimate the transformation relating two images remains largely unsolved. The present invention is an approach aimed at solving directly this fundamental problem by modeling the transformation as a general continuous 2-D mapping approximated by a parametric model. It includes method for estimating the parameters of the transformation in a computationally efficient manner involving a linear estimation problem rather than an extensive search. The solution is unique and is applicable to essentially any continuous transformation regardless of the magnitude of the deformation. Once the transformation (or its inverse) has been estimated, one can map one image onto the other, i.e. perform image registration and recognition.
This provides a solution to the problem of estimating the deformation between two images, a basic building block that can facilitate the solution and implementation of many existing open problems including the general problem of automatic recognition of objects.
In automatic recognition a measured image needs to be compared to a library of templates. This problem is greatly complicated by the need to take into account the deformation between the template and the observation on the object. Using the method disclosed here, one can estimate this deformation and map the measured image so that it is ready to be matched with the template. This process will be carried out for each template in the library.
The solution has a wide range of applications to problems of interest in a wide range of areas, such as in automatic detection and recognition of deformations and anomalies in medical images; in security systems where claimed identity has to be verified by comparing an acquired image of a person or object to an existing database, or more challenging in systems where an object (such as a specific suspect) has to be identified from an input stream containing a large number of similar objects; in object based low bit rate image coding: most of the information on the moving objects in the scene can be faithfully described and tracked as a set of continuous transformations applied to a small set of templates providing the object appearance from various observation angles; or in remote sensing image registration where the problem becomes especially severe when images are taken at low angles and are therefore highly deformed by the perspective projection.
The solution is aimed at directly solving the fundamental problem common to this vast set of application areas and to many similar ones, namely estimating the deformation relating any given observed signature of the object to a pre-defined representation of it. The method provides an accurate yet computationally very simple solution to a problem for which existing solutions require an extensive numerical optimization, which is not guaranteed to provide the correct solution.